missing file

lettersController.py

@@ -4,17 +4,17 @@ import pickle
 import tensorflow as tf
 import mediapipe as mp
 
-lettersModel = tf.keras.models.load_model('ai_model/models/detectLettersModel.keras')
-with open('ai_model/models/labelEncoder.pickle', 'rb') as f:
-    labelEncoder = pickle.load(f)
+# lettersModel = tf.keras.models.load_model('ai_model/models/detectLettersModel.keras')
+# with open('ai_model/models/labelEncoder.pickle', 'rb') as f:
+#     labelEncoder = pickle.load(f)
 
-lettersModel2 = tf.keras.models.load_model('ai_model/jz_model/JZModel.keras')
-with open('ai_model/jz_model/labelEncoder.pickle', 'rb') as f:
-    labelEncoder2 = pickle.load(f)
+# lettersModel2 = tf.keras.models.load_model('ai_model/jz_model/JZModel.keras')
+# with open('ai_model/jz_model/labelEncoder.pickle', 'rb') as f:
+#     labelEncoder2 = pickle.load(f)
 
-numbersModel = tf.keras.models.load_model('ai_model/models/detectNumbersModel.keras')
-with open('ai_model/models/numLabelEncoder.pickle', 'rb') as f:
-    numLabelEncoder = pickle.load(f)
+# numbersModel = tf.keras.models.load_model('ai_model/models/detectNumbersModel.keras')
+# with open('ai_model/models/numLabelEncoder.pickle', 'rb') as f:
+#     numLabelEncoder = pickle.load(f)
 
 sequenceNum = 20
 hands = mp.solutions.hands.Hands(static_image_mode=True)

wordsController.py

@@ -10,10 +10,13 @@ SEQUENCE_LENGTH = 90
 EXPECTED_COORDS_PER_FRAME = 1662
 CONFIDENCE_THRESHOLD = 0.1
 
-model = load_model(MODEL_PATH)
-df = pd.read_csv(CSV_PATH)
-unique_glosses = df['gloss'].unique()
-id_to_gloss = {i: g for i, g in enumerate(unique_glosses)}
+# model = load_model(MODEL_PATH)
+# df = pd.read_csv(CSV_PATH)
+# unique_glosses = df['gloss'].unique()
+# id_to_gloss = {i: g for i, g in enumerate(unique_glosses)}
+# Insert these lines immediately after the commented-out block
+model = None # Placeholder
+id_to_gloss = {0: "placeholder_word"} # Minimal placeholder for the dictionary
 
 mp_holistic = mp.solutions.holistic.Holistic(
     static_image_mode=True,
@@ -129,11 +132,15 @@ def detectWords(image_paths):
     sequence = pad_or_truncate_sequence(sequence, SEQUENCE_LENGTH, EXPECTED_COORDS_PER_FRAME)
     sequence = np.expand_dims(sequence, axis=0) 
 
-    preds = model.predict(sequence, verbose=0)
-    predicted_id = int(np.argmax(preds))
-    confidence = float(np.max(preds))
-    predicted_word = id_to_gloss.get(predicted_id, "Unknown")
+    # preds = model.predict(sequence, verbose=0)
+    # predicted_id = int(np.argmax(preds))
+    # confidence = float(np.max(preds))
+    # predicted_word = id_to_gloss.get(predicted_id, "Unknown")
 
+    predicted_id = 0 # Use the placeholder ID
+    confidence = 0.99 # Use a dummy confidence
+    predicted_word = id_to_gloss.get(predicted_id, "Unknown")
+    
     result = {"word": predicted_word if confidence >= CONFIDENCE_THRESHOLD else "",
               "confidence": confidence}